{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "!pip install -Uqq fastbook\n",
    "import fastbook\n",
    "fastbook.setup_book()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from fastbook import *"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[[chapter_multicat]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other Computer Vision Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous chapter you learned some important practical techniques for training models in practice. Considerations like selecting learning rates and the number of epochs are very important to getting good results.\n",
    "\n",
    "In this chapter we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one is when you want to predict more than one label per image (or sometimes none at all), and the second is when your labels are one or several numbers—a quantity instead of a category.\n",
    "\n",
    "In the process will study more deeply the output activations, targets, and loss functions in deep learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Label Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-label classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. There may be more than one kind of object, or there may be no objects at all in the classes that you are looking for.\n",
    "\n",
    "For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out in <<chapter_production>> was that if a user uploaded something that wasn't any kind of bear, the model would still say it was either a grizzly, black, or teddy bear—it had no ability to predict \"not a bear at all.\" In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application, and try to retrain it using the multi-label technique, then test it by passing in an image that is not of any of your recognized classes.\n",
    "\n",
    "In practice, we have not seen many examples of people training multi-label classifiers for this purpose—but we very often see both users and developers complaining about this problem. It appears that this simple solution is not at all widely understood or appreciated! Because in practice it is probably more common to have some images with zero matches or more than one match, we should probably expect in practice that multi-label classifiers are more widely applicable than single-label classifiers.\n",
    "\n",
    "First, let's see what a multi-label dataset looks like, then we'll explain how to get it ready for our model. You'll see that the architecture of the model does not change from the last chapter; only the loss function does. Let's start with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example we are going to use the PASCAL dataset, which can have more than one kind of classified object per image.\n",
    "\n",
    "We begin by downloading and extracting the dataset as per usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "path = untar_data(URLs.PASCAL_2007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is different from the ones we have seen before, in that it is not structured by filename or folder but instead comes with a CSV (comma-separated values) file telling us what labels to use for each image. We can inspect the CSV file by reading it into a Pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fname</th>\n",
       "      <th>labels</th>\n",
       "      <th>is_valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000005.jpg</td>\n",
       "      <td>chair</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000007.jpg</td>\n",
       "      <td>car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000009.jpg</td>\n",
       "      <td>horse person</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000012.jpg</td>\n",
       "      <td>car</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000016.jpg</td>\n",
       "      <td>bicycle</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fname        labels  is_valid\n",
       "0  000005.jpg         chair      True\n",
       "1  000007.jpg           car      True\n",
       "2  000009.jpg  horse person      True\n",
       "3  000012.jpg           car     False\n",
       "4  000016.jpg       bicycle      True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path/'train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the list of categories in each image is shown as a space-delimited string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sidebar: Pandas and DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analyze tabular and time series data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, Python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as shown here.\n",
    "\n",
    "You can access rows and columns of a DataFrame with the `iloc` property, as if it were a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       000005.jpg\n",
       "1       000007.jpg\n",
       "2       000009.jpg\n",
       "3       000012.jpg\n",
       "4       000016.jpg\n",
       "           ...    \n",
       "5006    009954.jpg\n",
       "5007    009955.jpg\n",
       "5008    009958.jpg\n",
       "5009    009959.jpg\n",
       "5010    009961.jpg\n",
       "Name: fname, Length: 5011, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fname       000005.jpg\n",
       "labels           chair\n",
       "is_valid          True\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]\n",
    "# Trailing :s are always optional (in numpy, pytorch, pandas, etc.),\n",
    "#   so this is equivalent:\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also grab a column by name by indexing into a DataFrame directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       000005.jpg\n",
       "1       000007.jpg\n",
       "2       000009.jpg\n",
       "3       000012.jpg\n",
       "4       000016.jpg\n",
       "           ...    \n",
       "5006    009954.jpg\n",
       "5007    009955.jpg\n",
       "5008    009958.jpg\n",
       "5009    009959.jpg\n",
       "5010    009961.jpg\n",
       "Name: fname, Length: 5011, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fname']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create new columns and do calculations using columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  3\n",
       "1  2  4"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n",
    "tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  3  4\n",
       "1  2  4  6"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df['c'] = tmp_df['a']+tmp_df['b']\n",
    "tmp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a fast and flexible library, and an important part of every data scientist’s Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you haven’t used Pandas before, we’d suggest going through a tutorial; we are particularly fond of the book [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney, the creator of Pandas (O'Reilly). It also covers other important libraries like `matplotlib` and `numpy`. We will try to briefly describe Pandas functionality we use as we come across it, but will not go into the level of detail of McKinney’s book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End sidebar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have seen what the data looks like, let's make it ready for model training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing a DataBlock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we convert from a `DataFrame` object to a `DataLoaders` object? We generally suggest using the data block API for creating a `DataLoaders` object, where possible, since it provides a good mix of flexibility and simplicity. Here we will show you the steps that we take to use the data blocks API to construct a `DataLoaders` object in practice, using this dataset as an example.\n",
    "\n",
    "As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n",
    "\n",
    "- `Dataset`:: A collection that returns a tuple of your independent and dependent variable for a single item\n",
    "- `DataLoader`:: An iterator that provides a stream of mini-batches, where each mini-batch is a tuple of a batch of independent variables and a batch of dependent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
    "\n",
    "- `Datasets`:: An object that contains a training `Dataset` and a validation `Dataset`\n",
    "- `DataLoaders`:: An object that contains a training `DataLoader` and a validation `DataLoader`\n",
    "\n",
    "Since a `DataLoader` builds on top of a `Dataset` and adds additional functionality to it (collating multiple items into a mini-batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we create a `DataBlock`, we build up gradually, step by step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. It’s easy to debug, because you know that if a problem arises, it is in the line of code you just typed!\n",
    "\n",
    "Let’s start with the simplest case, which is a data block created with no parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dblock = DataBlock()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a `Datasets` object from this. The only thing needed is a source—in this case, our DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsets = dblock.datasets(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This contains a `train` and a `valid` dataset, which we can index into:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4009, 1002)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dsets.train),len(dsets.valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(fname       008663.jpg\n",
       " labels      car person\n",
       " is_valid         False\n",
       " Name: 4346, dtype: object,\n",
       " fname       008663.jpg\n",
       " labels      car person\n",
       " is_valid         False\n",
       " Name: 4346, dtype: object)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = dsets.train[0]\n",
    "x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this simply returns a row of the DataFrame, twice. This is because by default, the data block assumes we have two things: input and target. We are going to need to grab the appropriate fields from the DataFrame, which we can do by passing `get_x` and `get_y` functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'008663.jpg'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['fname']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('005620.jpg', 'aeroplane')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dblock = DataBlock(get_x = lambda r: r['fname'], get_y = lambda r: r['labels'])\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, rather than defining a function in the usual way, we are using Python’s `lambda` keyword. This is just a shortcut for defining and then referring to a function. The following more verbose approach is identical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('002549.jpg', 'tvmonitor')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_x(r): return r['fname']\n",
    "def get_y(r): return r['labels']\n",
    "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lambda functions are great for quickly iterating, but they are not compatible with serialization, so we advise you to use the more verbose approach if you want to export your `Learner` after training (lambdas are fine if you are just experimenting)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the dependent variable will need to be split on the space character (which is the default for Python’s `split` function) so that it becomes a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Path('/home/jhoward/.fastai/data/pascal_2007/train/002844.jpg'), ['train'])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_x(r): return path/'train'/r['fname']\n",
    "def get_y(r): return r['labels'].split(' ')\n",
    "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To actually open the image and do the conversion to tensors, we will need to use a set of transforms; block types will provide us with those. We can use the same block types that we have used previously, with one exception: the `ImageBlock` will work fine again, because we have a path that points to a valid image, but the `CategoryBlock` is not going to work. The problem is that block returns a single integer, but we need to be able to have multiple labels for each item. To solve this, we use a `MultiCategoryBlock`. This type of block expects to receive a list of strings, as we have in this case, so let’s test it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PILImage mode=RGB size=500x375,\n",
       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   get_x = get_x, get_y = get_y)\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our list of categories is not encoded in the same way that it was for the regular `CategoryBlock`. In that case, we had a single integer representing which category was present, based on its location in our vocab. In this case, however, we instead have a list of zeros, with a one in any position where that category is present. For example, if there is a one in the second and fourth positions, then that means that vocab items two and four are present in this image. This is known as *one-hot encoding*. The reason we can’t easily just use a list of category indices is that each list would be a different length, and PyTorch requires tensors, where everything has to be the same length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: One-hot encoding: Using a vector of zeros, with a one in each location that is represented in the data, to encode a list of integers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check what the categories represent for this example (we are using the convenient `torch.where` function, which tells us all of the indices where our condition is true or false):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) ['dog']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs = torch.where(dsets.train[0][1]==1.)[0]\n",
    "dsets.train.vocab[idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With NumPy arrays, PyTorch tensors, and fastai’s `L` class, we can index directly using a list or vector, which makes a lot of code (such as this example) much clearer and more concise.\n",
    "\n",
    "We have ignored the column `is_valid` up until now, which means that `DataBlock` has been using a random split by default. To explicitly choose the elements of our validation set, we need to write a function and pass it to `splitter` (or use one of fastai's predefined functions or classes). It will take the items (here our whole DataFrame) and must return two (or more) lists of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PILImage mode=RGB size=500x333,\n",
       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def splitter(df):\n",
    "    train = df.index[~df['is_valid']].tolist()\n",
    "    valid = df.index[df['is_valid']].tolist()\n",
    "    return train,valid\n",
    "\n",
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   splitter=splitter,\n",
    "                   get_x=get_x, \n",
    "                   get_y=get_y)\n",
    "\n",
    "dsets = dblock.datasets(df)\n",
    "dsets.train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have discussed, a `DataLoader` collates the items from a `Dataset` into a mini-batch. This is a tuple of tensors, where each tensor simply stacks the items from that location in the `Dataset` item. \n",
    "\n",
    "Now that we have confirmed that the individual items look okay, there's one more step we need to ensure we can create our `DataLoaders`, which is to ensure that every item is of the same size. To do this, we can use `RandomResizedCrop`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
    "                   splitter=splitter,\n",
    "                   get_x=get_x, \n",
    "                   get_y=get_y,\n",
    "                   item_tfms = RandomResizedCrop(128, min_scale=0.35))\n",
    "dls = dblock.dataloaders(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can display a sample of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(nrows=1, ncols=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that if anything goes wrong when you create your `DataLoaders` from your `DataBlock`, or if you want to view exactly what happens with your `DataBlock`, you can use the `summary` method we presented in the last chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is now ready for training a model. As we will see, nothing is going to change when we create our `Learner`, but behind the scenes, the fastai library will pick a new loss function for us: binary cross-entropy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Cross-Entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create our `Learner`. We saw in <<chapter_mnist_basics>> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already have our `DataLoaders`, we can leverage fastai's `resnet` models (which we'll learn how to create from scratch later), and we know how to create an `SGD` optimizer. So let's focus on ensuring we have a suitable loss function. To do this, let's use `cnn_learner` to create a `Learner`, so we can look at its activations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(dls, resnet18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also saw that the model in a `Learner` is generally an object of a class inheriting from `nn.Module`, and that we can call it using parentheses and it will return the activations of a model. You should pass it your independent variable, as a mini-batch. We can try it out by grabbing a mini batch from our `DataLoader` and then passing it to the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 20])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = to_cpu(dls.train.one_batch())\n",
    "activs = learn.model(x)\n",
    "activs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Think about why `activs` has this shape—we have a batch size of 64, and we need to calculate the probability of each of 20 categories. Here’s what one of those activations looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorImage([ 0.7476, -1.1988,  4.5421, -1.5915, -0.6749,  0.0343, -2.4930, -0.8330, -0.3817, -1.4876, -0.1683,  2.1547, -3.4151, -1.1743,  0.1530, -1.6801, -2.3067,  0.7063, -1.3358, -0.3715],\n",
       "       grad_fn=<AliasBackward>)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> note: Getting Model Activations: Knowing how to manually get a mini-batch and pass it into a model, and look at the activations and loss, is really important for debugging your model. It is also very helpful for learning, so that you can see exactly what is going on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They aren’t yet scaled to between 0 and 1, but we learned how to do that in <<chapter_mnist_basics>>, using the `sigmoid` function. We also saw how to calculate a loss based on this—this is our loss function from <<chapter_mnist_basics>>, with the addition of `log` as discussed in the last chapter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_cross_entropy(inputs, targets):\n",
    "    inputs = inputs.sigmoid()\n",
    "    return -torch.where(targets==1, 1-inputs, inputs).log().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that because we have a one-hot-encoded dependent variable, we can't directly use `nll_loss` or `softmax` (and therefore we can't use `cross_entropy`):\n",
    "\n",
    "- `softmax`, as we saw, requires that all predictions sum to 1, and tends to push one activation to be much larger than the others (due to the use of `exp`); however, we may well have multiple objects that we're confident appear in an image, so restricting the maximum sum of activations to 1 is not a good idea. By the same reasoning, we may want the sum to be *less* than 1, if we don't think *any* of the categories appear in an image.\n",
    "- `nll_loss`, as we saw, returns the value of just one activation: the single activation corresponding with the single label for an item. This doesn't make sense when we have multiple labels.\n",
    "\n",
    "On the other hand, the `binary_cross_entropy` function, which is just `mnist_loss` along with `log`, provides just what we need, thanks to the magic of PyTorch's elementwise operations. Each activation will be compared to each target for each column, so we don't have to do anything to make this function work for multiple columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> j: One of the things I really like about working with libraries like PyTorch, with broadcasting and elementwise operations, is that quite frequently I find I can write code that works equally well for a single item or a batch of items, without changes. `binary_cross_entropy` is a great example of this. By using these operations, we don't have to write loops ourselves, and can rely on PyTorch to do the looping we need as appropriate for the rank of the tensors we're working with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyTorch already provides this function for us. In fact, it provides a number of versions, with rather confusing names!\n",
    "\n",
    "`F.binary_cross_entropy` and its module equivalent `nn.BCELoss` calculate cross-entropy on a one-hot-encoded target, but do not include the initial `sigmoid`. Normally for one-hot-encoded targets you'll want `F.binary_cross_entropy_with_logits` (or `nn.BCEWithLogitsLoss`), which do both sigmoid and binary cross-entropy in a single function, as in the preceding example.\n",
    "\n",
    "The equivalent for single-label datasets (like MNIST or the Pet dataset), where the target is encoded as a single integer, is `F.nll_loss` or `nn.NLLLoss` for the version without the initial softmax, and `F.cross_entropy` or `nn.CrossEntropyLoss` for the version with the initial softmax.\n",
    "\n",
    "Since we have a one-hot-encoded target, we will use `BCEWithLogitsLoss`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorImage(1.0342, grad_fn=<AliasBackward>)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_func = nn.BCEWithLogitsLoss()\n",
    "loss = loss_func(activs, y)\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't actually need to tell fastai to use this loss function (although we can if we want) since it will be automatically chosen for us. fastai knows that the `DataLoaders` has multiple category labels, so it will use `nn.BCEWithLogitsLoss` by default.\n",
    "\n",
    "One change compared to the last chapter is the metric we use: because this is a multilabel problem, we can't use the accuracy function. Why is that? Well, accuracy was comparing our outputs to our targets like so:\n",
    "\n",
    "```python\n",
    "def accuracy(inp, targ, axis=-1):\n",
    "    \"Compute accuracy with `targ` when `pred` is bs * n_classes\"\n",
    "    pred = inp.argmax(dim=axis)\n",
    "    return (pred == targ).float().mean()\n",
    "```\n",
    "\n",
    "The class predicted was the one with the highest activation (this is what `argmax` does). Here it doesn't work because we could have more than one prediction on a single image. After applying the sigmoid to our activations (to make them between 0 and 1), we need to decide which ones are 0s and which ones are 1s by picking a *threshold*. Each value above the threshold will be considered as a 1, and each value lower than the threshold will be considered a 0:\n",
    "\n",
    "```python\n",
    "def accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n",
    "    \"Compute accuracy when `inp` and `targ` are the same size.\"\n",
    "    if sigmoid: inp = inp.sigmoid()\n",
    "    return ((inp>thresh)==targ.bool()).float().mean()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we pass `accuracy_multi` directly as a metric, it will use the default value for `threshold`, which is 0.5. We might want to adjust that default and create a new version of `accuracy_multi` that has a different default. To help with this, there is a function in Python called `partial`. It allows us to *bind* a function with some arguments or keyword arguments, making a new version of that function that, whenever it is called, always includes those arguments. For instance, here is a simple function taking two arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Hello Jeremy.', 'Ahoy! Jeremy.')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def say_hello(name, say_what=\"Hello\"): return f\"{say_what} {name}.\"\n",
    "say_hello('Jeremy'),say_hello('Jeremy', 'Ahoy!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can switch to a French version of that function by using `partial`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Bonjour Jeremy.', 'Bonjour Sylvain.')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = partial(say_hello, say_what=\"Bonjour\")\n",
    "f(\"Jeremy\"),f(\"Sylvain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now train our model. Let's try setting the accuracy threshold to 0.2 for our metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy_multi</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.942663</td>\n",
       "      <td>0.703737</td>\n",
       "      <td>0.233307</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.821548</td>\n",
       "      <td>0.550827</td>\n",
       "      <td>0.295319</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.604189</td>\n",
       "      <td>0.202585</td>\n",
       "      <td>0.816474</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.359258</td>\n",
       "      <td>0.123299</td>\n",
       "      <td>0.944283</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy_multi</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.135746</td>\n",
       "      <td>0.123404</td>\n",
       "      <td>0.944442</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.118443</td>\n",
       "      <td>0.107534</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.098525</td>\n",
       "      <td>0.104778</td>\n",
       "      <td>0.951554</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
    "learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Picking a threshold is important. If you pick a threshold that's too low, you'll often be failing to select correctly labeled objects. We can see this by changing our metric, and then calling `validate`, which returns the validation loss and metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(#2) [0.10477833449840546,0.9314740300178528]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.metrics = partial(accuracy_multi, thresh=0.1)\n",
    "learn.validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you pick a threshold that's too high, you'll only be selecting the objects for which your model is very confident:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(#2) [0.10477833449840546,0.9429482221603394]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.metrics = partial(accuracy_multi, thresh=0.99)\n",
    "learn.validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find the best threshold by trying a few levels and seeing what works best. This is much faster if we just grab the predictions once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds,targs = learn.get_preds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can call the metric directly. Note that by default `get_preds` applies the output activation function (sigmoid, in this case) for us, so we'll need to tell `accuracy_multi` to not apply it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorImage(0.9567)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_multi(preds, targs, thresh=0.9, sigmoid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use this approach to find the best threshold level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = torch.linspace(0.05,0.95,29)\n",
    "accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]\n",
    "plt.plot(xs,accs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. Sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this).\n",
    "\n",
    "This concludes the part of this chapter dedicated to multi-label classification. Next, we'll take a look at a regression problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy to think of deep learning models as being classified into domains, like *computer vision*, *NLP*, and so forth. And indeed, that's how fastai classifies its applications—largely because that's how most people are used to thinking of things.\n",
    "\n",
    "But really, that's hiding a more interesting and deeper perspective. A model is defined by its independent and dependent variables, along with its loss function. That means that there's really a far wider array of models than just the simple domain-based split. Perhaps we have an independent variable that's an image, and a dependent that's text (e.g., generating a caption from an image); or perhaps we have an independent variable that's text and dependent that's an image (e.g., generating an image from a caption—which is actually possible for deep learning to do!); or perhaps we've got images, texts, and tabular data as independent variables, and we're trying to predict product purchases... the possibilities really are endless.\n",
    "\n",
    "To be able to move beyond fixed applications, to crafting your own novel solutions to novel problems, it helps to really understand the data block API (and maybe also the mid-tier API, which we'll see later in the book). As an example, let's consider the problem of *image regression*. This refers to learning from a dataset where the independent variable is an image, and the dependent variable is one or more floats. Often we see people treat image regression as a whole separate application—but as you'll see here, we can treat it as just another CNN on top of the data block API.\n",
    "\n",
    "We're going to jump straight to a somewhat tricky variant of image regression, because we know you're ready for it! We're going to do a key point model. A *key point* refers to a specific location represented in an image—in this case, we'll use images of people and we'll be looking for the center of the person's face in each image. That means we'll actually be predicting *two* values for each image: the row and column of the face center. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assemble the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the [Biwi Kinect Head Pose dataset](https://icu.ee.ethz.ch/research/datsets.html) for this section. We'll begin by downloading the dataset as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.BIWI_HEAD_POSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "Path.BASE_PATH = path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we've got!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls().sorted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 24 directories numbered from 01 to 24 (they correspond to the different people photographed), and a corresponding *.obj* file for each (we won't need them here). Let's take a look inside one of these directories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path/'01').ls().sorted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside the subdirectories, we have different frames, each of them come with an image (*\\_rgb.jpg*) and a pose file (*\\_pose.txt*). We can easily get all the image files recursively with `get_image_files`, then write a function that converts an image filename to its associated pose file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('13/frame_00349_pose.txt')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_files = get_image_files(path)\n",
    "def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')\n",
    "img2pose(img_files[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at our first image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create(img_files[0])\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x120 at 0x7F2DF0A49690>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.to_thumb(160)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Biwi dataset website used to explain the format of the pose text file associated with each image, which shows the location of the center of the head. The details of this aren't important for our purposes, so we'll just show the function we use to extract the head center point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)\n",
    "def get_ctr(f):\n",
    "    ctr = np.genfromtxt(img2pose(f), skip_header=3)\n",
    "    c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]\n",
    "    c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]\n",
    "    return tensor([c1,c2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns the coordinates as a tensor of two items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([384.6370, 259.4787])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ctr(img_files[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass this function to `DataBlock` as `get_y`, since it is responsible for labeling each item. We'll resize the images to half their input size, just to speed up training a bit.\n",
    "\n",
    "One important point to note is that we should not just use a random splitter. The reason for this is that the same people appear in multiple images in this dataset, but we want to ensure that our model can generalize to people that it hasn't seen yet. Each folder in the dataset contains the images for one person. Therefore, we can create a splitter function that returns true for just one person, resulting in a validation set containing just that person's images.\n",
    "\n",
    "The only other difference from the previous data block examples is that the second block is a `PointBlock`. This is necessary so that fastai knows that the labels represent coordinates; that way, it knows that when doing data augmentation, it should do the same augmentation to these coordinates as it does to the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "biwi = DataBlock(\n",
    "    blocks=(ImageBlock, PointBlock),\n",
    "    get_items=get_image_files,\n",
    "    get_y=get_ctr,\n",
    "    splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
    "    batch_tfms=[*aug_transforms(size=(240,320)), \n",
    "                Normalize.from_stats(*imagenet_stats)]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> important: Points and Data Augmentation: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So, if you're working with another library, you may need to disable data augmentation for these kinds of problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before doing any modeling, we should look at our data to confirm it seems okay:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls = biwi.dataloaders(path)\n",
    "dls.show_batch(max_n=9, figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's looking good! As well as looking at the batch visually, it's a good idea to also look at the underlying tensors (especially as a student; it will help clarify your understanding of what your model is really seeing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb,yb = dls.one_batch()\n",
    "xb.shape,yb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you understand *why* these are the shapes for our mini-batches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an example of one row from the dependent variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorPoint([[-0.3375,  0.2193]], device='cuda:6')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we haven't had to use a separate *image regression* application; all we've had to do is label the data, and tell fastai what kinds of data the independent and dependent variables represent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's the same for creating our `Learner`. We will use the same function as before, with one new parameter, and we will be ready to train our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we can use `cnn_learner` to create our `Learner`. Remember way back in <<chapter_intro>> how we used `y_range` to tell fastai the range of our targets? We'll do the same here (coordinates in fastai and PyTorch are always rescaled between -1 and +1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(dls, resnet18, y_range=(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`y_range` is implemented in fastai using `sigmoid_range`, which is defined as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_range(x, lo, hi): return torch.sigmoid(x) * (hi-lo) + lo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is set as the final layer of the model, if `y_range` is defined. Take a moment to think about what this function does, and why it forces the model to output activations in the range `(lo,hi)`.\n",
    "\n",
    "Here's what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhoward/anaconda3/lib/python3.7/site-packages/fastbook/__init__.py:55: UserWarning: Not providing a value for linspace's steps is deprecated and will throw a runtime error in a future release. This warning will appear only once per process. (Triggered internally at  /pytorch/aten/src/ATen/native/RangeFactories.cpp:23.)\n",
      "  x = torch.linspace(min,max)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD7CAYAAABwggP9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAoHklEQVR4nO3deXxV1bn/8c8DYQiEEAIhjGFGRpmCsxVr61yhYlsUEaRKRW21t7Xqrd62atX6a+3oRKvizNWKVetUZ4taMQwBQQjIEAYhA5CRJCR5fn8k8caYEAI72SfJ9/16nRc5a6+9eJKcc56svdbay9wdERGRmtqEHYCIiEQmJQgREamVEoSIiNRKCUJERGqlBCEiIrWKCjuAIPXo0cMHDhwYdhgiIs3KsmXLstw9oWZ5i0oQAwcOJCUlJewwRESaFTPbWlu5LjGJiEitAk0QZna1maWYWbGZLayn7o/NbJeZ5ZjZQ2bWodqxeDN7zswKzGyrmV0UZJwiIlK/oHsQO4HbgIcOVsnMzgBuAE4DBgKDgV9Vq3IPUAIkAjOB+8xsdMCxiojIQQSaINx9sbv/A8iup+ps4EF3X+Pue4FbgTkAZtYZmA7c7O757r4EeAGYFWSsIiJycGGNQYwGUqs9TwUSzaw7MBwoc/e0GsfVgxARaUJhJYgYIKfa86qvu9RyrOp4l9oaMrN5leMeKZmZmYEHKiLSWoWVIPKB2GrPq77Oq+VY1fG82hpy9wXunuzuyQkJX5nGKyIihymsdRBrgHHA05XPxwG73T3bzIqAKDMb5u4bqh1fE0KcIiIRpaS0nIy8InbnFpGRW1zxb14xPzhlCF2j2wX6fwWaIMwsqrLNtkBbM+sIlLp7aY2qjwILzewJ4HPgJmAhgLsXmNli4BYzuwwYD0wFTggyVhGRSJSz/wDb9hSyfe9+tu8tZMe+/Xy+r4idOfvZua+I7IJiam7j07aNMW1C38hOEFR80P+i2vOLgV+Z2UPAWmCUu6e7+6tmdhfwNhANPFvjvCupmCqbQcWMqPnurh6EiLQIeUUH+CyzgM1Z+WzOKmRzVgFbswtI31PIvsIDX6rbqX1b+sZF0zsumlG9Y+nVtSO9YjuS2LUjiV060jO2A/Gd2tOmjQUep7WkHeWSk5Ndt9oQkUiRX1zK+l25rN+VT9ruPDZk5LExI5/ducVf1Glj0LdbNAO7dyYpvhMDuneif7dO9I/vRL9u0XSNbodZ8B/+1ZnZMndPrlneou7FJCISlqz8Yj7ZkcOanbl8siOHtZ/nsjW78Ivjndq3ZVjPGE4amsDQnjEMSejM4IQY+sdH0yGqbYiR100JQkSkgYpLy/hkRy7Lt+5l5fZ9rEzfx459+784PrB7J0b3ieWCif0Y2TuWo3p1oW9cdKNcBmpMShAiIvXILy5l2da9fLQpm6Wb97BqRw4lpeUA9I2LZnxSHHNOGMjYfl0Z1SeW2I7BDhaHRQlCRKSGktJyVqTv5f2NWSzZmEXq9hzKyp22bYyxfbsy+/gBTBrQjYkDutGzS8eww200ShAiIsCunCLeWpfBO+sz+OCzbPKLS2ljcHS/OK44ZTDHDe7OxKRudO7Qej42W893KiJSjbuTtjuf19bs4o1Pd7Nqe8UdfvrGRXPe+D6cMjyB4wZ3D3xtQXOiBCEirYa78+nneby0eievrN7FpqwCzGBC/zh+duZRfGNkIsN6xjT6tNLmQglCRFq8bXsKeSF1J/9YsYMNGfm0bWMcNzieuScN4vTRiS16HOFIKEGISItUWFLKK6t38fdl2/lwU8UWNZMHduPWaWM4e0wvusd0qKcFUYIQkRZl7c5cnly6lX+s2El+cSkDunfiJ98czrcn9qVft05hh9esKEGISLN3oKycVz7ZxcL3N7M8fR/to9pw7tjezDgmickDu2lM4TApQYhIs7WvsIQnPkrn0Q+3sDu3mIHdO3HTOSO5YFI/4jq1Dzu8Zk8JQkSanR379vPgvzez6ON0CkvKOHlYD+44fyxThvdsdreziGRKECLSbGzNLuDetz/j2eXbAThvXB8u/9pgRvauuQmlBEEJQkQiXnp2IX94M43nV+6kbRvj4uMGcPnXBtM3Ljrs0Fo0JQgRiVi7c4v481sbWLR0G23bGHNOGMgPvjaYnrFat9AUgt5yNB54EDgdyAJudPcna6l3PxW7zVVpB5S4e5fK4+8AxwFVW5XucPejgoxVRCJXfnEp97/zGX9bsonSMufCY5K4+utDSVRiaFJB9yDuAUqARCr2kn7JzFJrbhfq7lcAV1Q9N7OFQHmNtq52978FHJ+IRLCycmfRx+n8/vU0svJLOG9cH356+lEkddf6hTAEliDMrDMwHRjj7vnAEjN7AZgF3HAI550bVCwi0vykbNnD/zy/hrWf5zJ5YDf+Nnsy4/vHhR1WqxZkD2I4UObuadXKUoFT6jlvOpAJvFej/A4zuxNYD/zc3d+p7WQzmwfMA0hKSjqMsEUkTJl5xdzx8qcsXrGD3l078peLJnDO2N5a3BYBgkwQMUBOjbIcoEs9580GHnV3r1Z2PbCWistVM4AXzWy8u39W82R3XwAsAEhOTvaax0UkMpWXO0+nbOP2lz+l6EA5V506hKtOHUqn9po7EymC/E3kAzUnI8cCeXWdYGb9qehhXF693N0/qvb0ETO7EDgb+HMwoYpImDZl5nPDs6tZumUPxw6K59ffHsvQnjFhhyU1BJkg0oAoMxvm7hsqy8YBaw5yziXAB+6+qZ62HVB/U6SZKyt3Hlqymd/+az0d27XlrulH853kfrqcFKECSxDuXmBmi4FbzOwyKmYxTQVOOMhplwC/qV5gZnHAscC7VExz/R7wNeDaoGIVkaa3OauAnzy9kuXp+/jGyERu//YYrWeIcEFf7LsSeAjIALKB+e6+xsySqBhTGOXu6QBmdjzQD3imRhvtgNuAEUAZsA6Y5u7rA45VRJqAu/PU0m3c+s+1tI9qw++/N45p4/uq19AMBJog3H0PMK2W8nQqBrGrl30IdK6lbiYwOci4RCQcewpKuP7ZVby+djcnDu3O774znl5d1WtoLjRdQEQaxUebsvnRohXsLTjATeeMZO6Jg3Sn1WZGCUJEAlVe7tz7zkbufj2NAd078+DsyYzp2zXssOQwKEGISGD2FZZwzaKVvJuWyXnj+nD7+WOJ6aCPmeZKvzkRCcQnO3K44vFlZOQW8+tvj+GiY5I0EN3MKUGIyBFbvHw7Ny5eTXzn9jx9xfG6h1ILoQQhIoetrNy569V1PPDeJo4bHM9fLppIj5gOYYclAVGCEJHDkld0gGsWreStdRnMOm4A//OtUbRr2ybssCRAShAi0mA79u1n7sMfszEzn1unjmbW8QPDDkkagRKEiDTIJztymLvwY/aXlPHIpcdw0rAeYYckjUQJQkQO2dvrMrjqyeV069Sex+Yfy1G96rubvzRnShAickj+vmw71z+7ihG9uvDwnMm60V4roAQhIvV64N3PuOOVdZw0tAf3z5qkxW+thH7LIlInd+fOVyqmsZ57dG9+991xdIhqG3ZY0kSUIESkVuXlzk3Pf8KTH6Uz67gB/PK80bTVzfZaFSUIEfmK0rJyrvv7Kp5bsYP5U4bwszOO0m0zWiElCBH5kpLScn701ApeXbOL6844iqtOHRp2SBKSQJc9mlm8mT1nZgVmttXMLqqj3hwzKzOz/GqPKQ1tR0SCVVJazlVPLufVNbu4+dxRSg6tXNA9iHuAEiCRij2pXzKzVHdfU0vdD939pADaEZEAFJeWcdUTy3nj0wx+dd5oZp8wMOyQJGSB9SDMrDMwHbjZ3fPdfQnwAjArjHZE5NCVlJZ/kRxumarkIBWCvMQ0HChz97RqZanA6DrqTzCzLDNLM7ObzayqN9OgdsxsnpmlmFlKZmbmkX4PIq3OgbJyfvhURXK4ddoYLtF9laRSkAkiBsipUZYD1LYW/z1gDNCTit7ChcB1h9EO7r7A3ZPdPTkhIeEwQxdpncrKnf96OpXX1uzmf84dxazjBoQdkkSQIBNEPhBboywWyKtZ0d03uftmdy9399XALcAFDW1HRA5feblzw7OreDF1J9efOYK5Jw0KOySJMEEmiDQgysyGVSsbBxzKwLIDVZOsj6QdETkE7s4t/1zLM8u286PThjF/ypCwQ5IIFFiCcPcCYDFwi5l1NrMTganAYzXrmtlZZpZY+fUI4Gbg+Ya2IyKH509vbmThB1uYe+IgfvyNYfWfIK1S0Ns/XQlEAxnAU8B8d19jZkmVax2SKuudBqwyswLgZSoSwu31tRNwrCKt0sL3N/P7N9K4YFI/bjpnpFZIS53M3cOOITDJycmekpISdhgiEev5lTu4ZtFKTh+VyL0zJxKlLUIFMLNl7p5cs1yvDpFWYsmGLH76TCrHDornTxdOUHKQeukVItIKfLIjhx88lsKQhBgWXJJMx3a6ZbfUTwlCpIXbtqeQOQ9/TFyn9jwy9xi6RrcLOyRpJnQ3V5EWbF9hCbMfXsqBsnIWzTuORG0TKg2gHoRIC1VcWsa8x5axfc9+/npJMkN7xoQdkjQz6kGItEDl5c51z6xi6eY9/OnCCRwzKD7skKQZUg9CpAX6wxtpvJC6k5+deRTnjesTdjjSTClBiLQw/1ixgz+9tZHvJvdj/im6hYYcPiUIkRYkZcsefvb3VRw3OJ7bpo3VKmk5IkoQIi3Etj2FzHtsGX27RXP/xZNoH6W3txwZvYJEWoD84lIueySF0rJyHpydTFyn9mGHJC2AZjGJNHPl5c6P/3clGzPzeeTSYxicoOmsEgz1IESaubtfT+P1tbu56ZyRnDSsR9jhSAuiBCHSjL2YupO/vL2RGZP7M+eEgWGHIy2MEoRIM7V2Zy7X/T2V5AHduGXqGM1YksAFmiDMLN7MnjOzAjPbamYX1VFvtpktM7NcM9tuZneZWVS14++YWVHlJkP5ZrY+yDhFmru9BSXMeyyFuOj23HvxRM1YkkYR9KvqHqAESARmAveZ2eha6nUCrgV6AMdSscPcT2vUudrdYyofRwUcp0izVVpWztVPLScjr5j7Z02iZxfdgE8aR2CzmMysMzAdGOPu+cASM3sBmAXcUL2uu99X7ekOM3sCODWoWERast+8uo73N2Zz1wVHM75/XNjhSAsWZA9iOFDm7mnVylKB2noQNX0NqLnn9B1mlmVm75vZlLpONLN5ZpZiZimZmZkNjVmkWXkxdSd//fdmLjl+AN9N7h92ONLCBZkgYoCcGmU5QJeDnWRmlwLJwG+rFV8PDAb6AguAF82s1pvKuPsCd0929+SEhITDjV0k4qXtzuP6Z1cxaUA3bjpnVNjhSCsQZILIB2JrlMUCeXWdYGbTgDuBs9w9q6rc3T9y9zx3L3b3R4D3gbMDjFWkWcktOsAPHltGp/ZR3DtTg9LSNIJ8laUBUWY2rFrZOL566QgAMzsT+CvwLXdfXU/bDmgOn7RK7s5Pnk5l255C7p05UbvCSZMJLEG4ewGwGLjFzDqb2YnAVOCxmnXN7OvAE8B0d19a41icmZ1hZh3NLMrMZlIxRvFaULGKNCcPvLeJ19fu5sazR2rjH2lSQfdTrwSigQzgKWC+u68xs6TK9QxJlfVuBroCL1db6/BK5bF2wG1AJpAF/BCY5u5aCyGtzoefZXPXq+s45+jezD1xYNjhSCsT6M363H0PMK2W8nQqBrGrntc5pdXdM4HJQcYl0hztzi3ih08tZ2CPzvxm+tFaKS1NTiNdIhHoQFk5Vz+5nMKSMh64eBIxHXTjZWl6etWJRKD/99p6Pt6ylz/OGM+wxIPOFBdpNOpBiESYf63ZxYL3NnHxcUlMHd837HCkFVOCEIkg6dmF/OSZVMb27crN52oxnIRLCUIkQhSXlnHVk8sx4N6ZE+kQ1TbskKSV0xiESIT49UufsnpHDgtmTaJ/fKewwxFRD0IkEry06nMe/XArl500iNNH9wo7HBFACUIkdFuyCrj+2VVMSIrj+rNGhB2OyBeUIERCVHSgYtyhbRvjzxdOoF1bvSUlcmgMQiREv37pU9bszOVvlyTTr5vGHSSy6M8VkZC8tOpzHvvPVi4/eRDfGJUYdjgiX6EEIRKCrdn/N+7wszM17iCRSQlCpIkVl5Zx9ZMrNO4gEU9jECJN7I6X132x3kHjDhLJ9KeLSBN6bc0uFn6whbknar2DRD4lCJEmsn1vIdc9k8rR/bpyg9Y7SDMQaIIws3gze87MCsxsq5lddJC6PzazXWaWY2YPmVmHw2lHpDk4UFbOD59agTv85cKJtI/S32YS+YJ+ld4DlACJwEzgPjMbXbOSmZ0B3ACcBgwEBgO/amg7Is3Fb19bz4r0fdw5/WiSumvcQZqHwBKEmXUGpgM3u3u+uy8BXgBm1VJ9NvCgu69x973ArcCcw2hHJOK9vS6DByr3dzjn6N5hhyNyyILsQQwHytw9rVpZKlDbX/6jK49Vr5doZt0b2A5mNs/MUswsJTMz84i+AZGgfZ6zn/96eiUjenXhpnO0v4M0L0EmiBggp0ZZDlDbfok161Z93aWB7eDuC9w92d2TExISGhy0SGMpLSvnmkUrKS4t556ZE+nYTvs7SPMS5DqIfCC2RlkskHcIdau+zmtgOyIR609vbmDp5j3c/d1xDEmICTsckQYLsgeRBkSZ2bBqZeOANbXUXVN5rHq93e6e3cB2RCLSkg1Z/PntjXxnUj/On9gv7HBEDktgCcLdC4DFwC1m1tnMTgSmAo/VUv1R4PtmNsrMugE3AQsPox2RiJORV8S1/7uSoQkx/GqqJt9J8xX0NNcrgWggA3gKmO/ua8wsyczyzSwJwN1fBe4C3ga2Vj5+UV87AccqEriycufH/7uS/OID3DNzIp3a62420nwF+up19z3AtFrK06kYfK5edjdwd0PaEYl097y9kfc3ZnPn+WMZnljrvAqRZkPLOUUC8uFn2fzhjTSmju/D9yb3DzsckSOmBCESgKz8Yq5ZtIKB3Tvz62+PxczCDknkiOkCqcgRKq8cd9i3/wALLz2GmA56W0nLoB6EyBG6952N/HtDFr/41ihG9am5hEek+VKCEDkC/9mUzd2vp/GtcX246JiksMMRCZQShMhhyswr5kdPVYw73HG+xh2k5dHFUpHDULXeIWf/AR6Zq3EHaZn0qhY5DH95ayNLNmZx5/ljGdlb4w7SMukSk0gDLdmQxR/eTOP8CX213kFaNCUIkQbYlVPENYtWMKxnDLd9e4zGHaRFU4IQOUQV+0ovZ/+BMu7VfZakFdArXOQQ/b/X1vPxlr38ccZ4hvbUfZak5VMPQuQQvPrJ5yx4bxOXHD+AqeP7hh2OSJNQghCpx6bMfH76zCrG94/j5+eMDDsckSajBCFyEIUlpcx/fDnt2hr3zpxIhyjtKy2th8YgROrg7ty4eDVpGXk8OvcY+sRFhx2SSJMKpAdhZvFm9pyZFZjZVjO76CB1Z5vZMjPLNbPtZnaXmUVVO/6OmRVV7kCXb2brg4hRpKEefn8Lz6/cyU++OZyThyWEHY5IkwvqEtM9QAmQCMwE7jOzujbj7QRcC/QAjgVOA35ao87V7h5T+TgqoBhFDtnSzXu4/eVP+eaoRK6cMjTscERCccSXmMysMzAdGOPu+cASM3sBmAXcULO+u99X7ekOM3sCOPVI4xAJyu7cIq58YjlJ8Z343XfH0aaNFsNJ6xRED2I4UObuadXKUoG6ehA1fQ1YU6PsDjPLMrP3zWzKwU42s3lmlmJmKZmZmYcas0itikvLmP/4MgpLSrl/1iRiO7YLOySR0ASRIGKAnBplOUC9K4nM7FIgGfhtteLrgcFAX2AB8KKZDamrDXdf4O7J7p6ckKDrxHL43J1fPL+G5en7+N13xjE8UYvhpHWrN0FUDhp7HY8lQD5Q83aWsUBePe1OA+4EznL3rKpyd//I3fPcvdjdHwHeB85u4Pcl0mBPfJTOoo+3cdWpQzhrbO+wwxEJXb1jEO4+5WDHK8cgosxsmLtvqCwex1cvG1U/50zgr8A57r66vhAAXQSWRvXxlj388oU1nHpUAv/1Tc2LEIEALjG5ewGwGLjFzDqb2YnAVOCx2uqb2deBJ4Dp7r60xrE4MzvDzDqaWZSZzaRijOK1I41TpC479u3niseW0T++E3+YMYG2GpQWAYKb5nolEA1kAE8B8919DYCZJVWuZ6jasPdmoCvwcrW1Dq9UHmsH3AZkAlnAD4Fp7q61ENIoCktKueyRFErKyvnrJcl0jdagtEiVQFZSu/seYFodx9KpGMiuel7nlFZ3zwQmBxGTSH3Ky52fPJ3K+l25PDhnMkN7xtR/kkgronsxSav1xzc38Monu7jxrJGcelTPsMMRiThKENIqPb9yB398cwMXTOrHZScPCjsckYikBCGtzvL0vVz391UcMyie2789VtuGitRBCUJale17C5n3aAq9u3bk/osn0T5KbwGRuuh239Jq5BYd4PsLUyguLWfRvMnEd24fdkgiEU1/PkmrUFJazvzHl7EpK58HLp6kGUsih0A9CGnxqjb+eX9jNr/7zjhOGNoj7JBEmgX1IKTF++ObG3h2+XZ+/I3hTJ/UL+xwRJoNJQhp0Z5ams4f3qiYzvqj07Txj0hDKEFIi/X62t38/LnVTDkqgTvO13RWkYZSgpAWadnWPVz95HLG9u3KvTMn0q6tXuoiDaV3jbQ463blMndhCn3ionlozmQ6tddcDJHDoQQhLUp6diGzHlxKx3ZteHTuMXSP6RB2SCLNlv60khZjd24RMx/8DwfKynnmB8fTP75T2CGJNGvqQUiLsKeghFkPfsSe/BIWXnoMw7SftMgRCyRBmFm8mT1nZgVmttXMLjpI3TlmVlZts6B8M5tyOG2JAOQUHmDWgx+xNbuQv16SzPj+cWGHJNIiBHWJ6R6gBEgExgMvmVlq1a5ytfjQ3U8KqC1pxfKLS5n98FI27M5nwSWTtEpaJEBH3IMws87AdOBmd8939yXAC8CsMNuSlq+guJS5D3/MJzty+MtFE5iiTX9EAhXEJabhQJm7p1UrSwVGH+ScCWaWZWZpZnazmVX1ZBrclpnNM7MUM0vJzMw83O9BmpmC4lIuffhjlqXv5Q8zxnP66F5hhyTS4gSRIGKAnBplOUBdo4TvAWOAnlT0Fi4ErjvMtnD3Be6e7O7JCQkJDQxdmqP84lLmPLyUZel7+eOM8Zx7dJ+wQxJpkepNEGb2jpl5HY8lQD4QW+O0WCCvtvbcfZO7b3b3cndfDdwCXFB5uEFtSeuTV3SAOQ8tZXn6Pv40Y4KSg0gjqneQ2t2nHOx45bhBlJkNc/cNlcXjgEMdVHag6iY5aUfYlrRgewtKmP3wUtbuzOXPF07g7LG9ww5JpEU74ktM7l4ALAZuMbPOZnYiMBV4rLb6ZnaWmSVWfj0CuBl4/nDaktYjI6+IGQv+w7pdeTwwa5KSg0gTCGqh3JVANJABPAXMr5qWamZJlWsdkirrngasMrMC4GUqEsLth9KWtE7b9xYy44H/sG1vIQ/PmcxpIxPDDkmkVTB3DzuGwCQnJ3tKSkrYYUiA1u/K45KHPmJ/SRkPXzqZSQPiww5JpMUxs2XunlyzXPdikoiVsmUPcxd+THT7tjx9xfGM6FVz/oKINCYlCIlI/1qzix8tWkGfrtE8MvcY3XhPJARKEBJxHlqymVtfWsvR/eJ4aHaybtktEhIlCIkYZeXObS+t5eH3t3D6qET+OGMC0e3bhh2WSKulBCERIa/oANcsWslb6zKYe+Igfn7OSNq20R7SImFSgpDQbc0u4LJHUtiUVcCtU0cz6/iBYYckIihBSMg+2JjFlU8uxx0em3uMbtctEkGUICQU7s4D723irlfXMSQhhr9ekszAHp3DDktEqlGCkCaXX1zKdc+k8sonuzhnbG/uuuBoOnfQS1Ek0uhdKU1qzc4crn5yBel7Cvn52SO57ORBmGkwWiQSKUFIk3B3Hv8onVv/uZZundrx5GXHcuzg7mGHJSIHoQQhjW5vQQk3Ll7Nq2t2ccrwBO7+7jgtfhNpBpQgpFH9e0MmP3k6lb2FJdx41gguP3kwbbS+QaRZUIKQRlFYUspdr65n4QdbGNozhocvnczoPl3DDktEGkAJQgL30aZsfvbsKrZmFzLnhIHccNYIOrbTLTNEmhslCAlMXtEBfvvaeh75cCtJ8Z1YNO84jtNAtEizFciOcmYWb2bPmVmBmW01s4sOUvf+yh3mqh7FZpZX7fg7ZlZU7fj6IGKUxvXqJ7v45t3v8eh/tjLnhIG8eu3JSg4izVxQPYh7gBIgERgPvGRmqbVtFeruVwBXVD03s4VAeY1qV7v73wKKTRpRenYht/xzDW98msGIXl247+KJTEjqFnZYIhKAI04QZtYZmA6Mcfd8YImZvQDMAm44xHPPPdI4pGntLynjvnc/4/53PyOqjXHjWSOYe9Ig2rUNaptzEQlbED2I4UCZu6dVK0sFTjmEc6cDmcB7NcrvMLM7gfXAz939nboaMLN5wDyApKSkBoQth6O83HkhdSd3vbqOnTlFnDeuD/999kh6de0YdmgiErAgEkQMkFOjLAfocgjnzgYedXevVnY9sJaKS1YzgBfNbLy7f1ZbA+6+AFgAkJyc7LXVkWD8Z1M2t7/8Kau25zCmbyx3f2+8xhlEWrB6E4SZvUPdvYH3gR8CNXeTjwXyvlr9S+32r2z38url7v5RtaePmNmFwNnAn+uLVRrH6u05/PZf63k3LZPeXTty93fHMW18Xy14E2nh6k0Q7j7lYMcrxxGizGyYu2+oLB4HfGWAuoZLgA/cfVN9IQD6JArB2p25/PmtDbzyyS7iOrXjv88ewSXHD9SaBpFW4ogvMbl7gZktBm4xs8uomMU0FTihnlMvAX5TvcDM4oBjgXeBUuB7wNeAa480Tjl0q7fn8Ke3NvD62t106RDFj04bxuUnD6JLx3ZhhyYiTSioaa5XAg8BGUA2ML9qiquZJVExpjDK3dMry44H+gHP1GinHXAbMAIoA9YB09xdayEambuzZGMWD7y7iSUbs4jtGMW13xjGpScMomsnJQaR1iiQBOHue4BpdRxLp2Igu3rZh8BXtg9z90xgchAxyaEpOlDGP1d9zkNLNrP281x6dunA9WeOYOZxScSqxyDSqulWG63Ujn37WbQ0nSc/Sie7oIRhPWO4a/rRTJ3Qhw5RGmMQESWIVqW0rJx30zJ58qN03l6fgQOnjejJpScO4oQh3bWzm4h8iRJEK7AxI49nlm3nueU7yMgrpkdMB+ZPGcKMyUn0j+8UdngiEqGUIFqoXTlFvJi6k+dTd/DJjlzatjFOPSqBCyb147SRibolhojUSwmiBfk8Zz+vfrKLV1bv4uOte3CHo/t15aZzRjJ1fF8SumibTxE5dEoQzZi7s353Hm+s3c3rn2aQum0fACN6deGa04Zx3rg+DE6IOXgjIiJ1UIJoZvKKDvDBZ9m8sz6T99Iy2bFvPwDj+8dx3RlHceaYXgxRUhCRAChBRLiiA2WsSN/Hh5uyeX9jFiu37aOs3InpEMWJQ7tz9deHctqInvSM1d1URSRYShARJqfwAMvT95KydQ8fb97Lym37KCkrp43B0f3iuOKUwZw0NIFJA7rRPkoDzSLSeJQgQlRcWkbarnxWbt9H6rZ9rNy2j40Z+QC0bWOM7hPL7BMGcOyg7kweFE/XaK1sFpGmowTRRHIKD/Dprlw+/bzisWZnLmm78zhQVrGFRffO7RnfP45p4/swaUA84/p3pVN7/XpEJDz6BAqQu7OnoITPMgvYmJHPxox8NmTkkbY7j925xV/Ui+/cntF9Yrns5MGM6dOVo/t1pV+3aK1kFpGIogTRQGXlTkZeEdv27Cd9TyHp2QVs3VPIlqwCNmcVkFtU+kXdju3aMLRnDCcO6cGwxC6M6N2FUb1j6dmlg5KBiEQ8JYhqysudvYUl7M4tZlfufnblFLMrZz879hWxc99+dubsZ+e+/V9cFgJoY9C7azSDEzozdXxfBvbozNCeMQxJ6EyfrtHadU1Emi0lCOC/n1vN2+syyMwrprT8y9tatzHoFduR3nHRjO3blbPH9qZft2j6xkUzoHtn+sZFazaRiLRIgSQIM7samAOMBZ5y9zn11P8xcD0QDTxLxQZDxZXH4oEHgdOBLOBGd38yiDjr0jcumhOG9KBnbAcSu3SgZ2xHenXtSO+uHUmI6UCU7lskIq1QUD2InVTsBHcGFR/6dTKzM4AbgK9Xnvcc8KvKMoB7gBIgkYrtS18ys9SqHeoaw1WnDm2spkVEmq1A/jR298Xu/g8qthutz2zgQXdf4+57gVup6H1gZp2B6cDN7p7v7kuAF4BZQcQpIiKHLoxrJ6OB1GrPU4FEM+sODAfK3D2txvHRTRifiIgQToKIAXKqPa/6ukstx6qOd6mrMTObZ2YpZpaSmZkZaKAiIq1ZvQnCzN4xM6/jseQw/s98ILba86qv82o5VnU8r67G3H2Buye7e3JCQsJhhCMiIrWpN0G4+xR3tzoeJx3G/7kGGFft+Thgt7tnA2lAlJkNq3G80QaoRUSkdoFcYjKzKDPrCLQF2ppZRzOra4bUo8D3zWyUmXUDbgIWArh7AbAYuMXMOpvZicBU4LEg4hQRkUMX1BjETcB+KqaqXlz59U0AZpZkZvlmlgTg7q8CdwFvA1srH7+o1taVVEyVzQCeomKNhHoQIiJNzNy9/lrNRHJysqekpIQdhohIs2Jmy9w9+SvlLSlBmFkmFT2Sw9GDipXbkUZxNYziahjF1TAtNa4B7v6VWT4tKkEcCTNLqS2Dhk1xNYziahjF1TCtLS7dZEhERGqlBCEiIrVSgvg/C8IOoA6Kq2EUV8MoroZpVXFpDEJERGqlHoSIiNRKCUJERGqlBCEiIrVSgqiFmQ0zsyIzezzsWKqY2eNm9rmZ5ZpZmpldFgExdTCzB81sq5nlmdkKMzsr7LigYhvcytvAF5vZwhDjiDez58ysoPLndFFYsVQXKT+f6iL89RRx77/qGuszK6gtR1uae4CPww6ihjuA77t7sZmNAN4xsxXuvizEmKKAbcApQDpwNvC0mY119y0hxgUN2Aa3kTX5FrqHKFJ+PtVF8uspEt9/1TXKZ5Z6EDWY2QxgH/BmyKF8SeUWrcVVTysfQ0IMCXcvcPdfuvsWdy93938Cm4FJYcZVGVtDtsFtFJG8hW4k/HxqivDXU8S9/6o05meWEkQ1ZhYL3AL8JOxYamNm95pZIbAO+Bx4OeSQvsTMEqnYNjbsv44jhbbQPQKR9nqKxPdfY39mKUF82a3Ag+6+LexAauPuV1Kx/erJVOybUXzwM5qOmbUDngAecfd1YccTIRq8ha5UiMTXU4S+/xr1M6vVJIj6tk41s/HAN4DfR1ps1eu6e1nlpYp+wPxIiMvM2lCxqVMJcHVjxtSQuCJAg7fQlaZ/PTVEU77/6tMUn1mtZpDa3acc7LiZXQsMBNLNDCr++mtrZqPcfWKYsdUhika+BnoocVnFD+tBKgZhz3b3A40Z06HGFSG+2ELX3TdUlmkL3YMI4/V0mBr9/XcIptDIn1mtpgdxCBZQ8QsfX/m4H3iJilkeoTKznmY2w8xizKytmZ0BXAi8FXZswH3ASOBb7r4/7GCqWMO2wW0UkbyFbiT8fOoQca+nCH7/Nf5nlrvrUcsD+CXweNhxVMaSALxLxUyFXGA1cHkExDWAitkcRVRcTql6zIyA2H7J/802qXr8MoQ44oF/AAVUTN28KOyfTST9fJrD6ylS3391/E4D/czSzfpERKRWusQkIiK1UoIQEZFaKUGIiEitlCBERKRWShAiIlIrJQgREamVEoSIiNRKCUJERGr1/wHI7lOMx0S3pgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_function(partial(sigmoid_range,lo=-1,hi=1), min=-4, max=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We didn't specify a loss function, which means we're getting whatever fastai chooses as the default. Let's see what it picked for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlattenedLoss of MSELoss()"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls.loss_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes sense, since when coordinates are used as the dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
    "\n",
    "Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n",
    "\n",
    "We can pick a good learning rate with the learning rate finder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.033113110810518265)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try an LR of 1e-2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.049630</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>00:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.008714</td>\n",
       "      <td>0.004291</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.003213</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 1e-2\n",
    "learn.fine_tune(3, lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally when we run this we get a loss of around 0.0001, which corresponds to an average coordinate prediction error of:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(0.0001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sounds very accurate! But it's important to take a look at our results with `Learner.show_results`. The left side are the actual (*ground truth*) coordinates and the right side are our model's predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results(ds_idx=1, nrows=3, figsize=(6,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite amazing that with just a few minutes of computation we've created such an accurate key points model, and without any special domain-specific application. This is the power of building on flexible APIs, and using transfer learning! It's particularly striking that we've been able to use transfer learning so effectively even between totally different tasks; our pretrained model was trained to do image classification, and we fine-tuned for image regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In problems that are at first glance completely different (single-label classification, multi-label classification, and regression), we end up using the same model with just different numbers of outputs. The loss function is the one thing that changes, which is why it's important to double-check that you are using the right loss function for your problem.\n",
    "\n",
    "fastai will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your choice of loss function, and remember that you most probably want:\n",
    "\n",
    "- `nn.CrossEntropyLoss` for single-label classification\n",
    "- `nn.BCEWithLogitsLoss` for multi-label classification\n",
    "- `nn.MSELoss` for regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. How could multi-label classification improve the usability of the bear classifier?\n",
    "1. How do we encode the dependent variable in a multi-label classification problem?\n",
    "1. How do you access the rows and columns of a DataFrame as if it was a matrix?\n",
    "1. How do you get a column by name from a DataFrame?\n",
    "1. What is the difference between a `Dataset` and `DataLoader`?\n",
    "1. What does a `Datasets` object normally contain?\n",
    "1. What does a `DataLoaders` object normally contain?\n",
    "1. What does `lambda` do in Python?\n",
    "1. What are the methods to customize how the independent and dependent variables are created with the data block API?\n",
    "1. Why is softmax not an appropriate output activation function when using a one hot encoded target?\n",
    "1. Why is `nll_loss` not an appropriate loss function when using a one-hot-encoded target?\n",
    "1. What is the difference between `nn.BCELoss` and `nn.BCEWithLogitsLoss`?\n",
    "1. Why can't we use regular accuracy in a multi-label problem?\n",
    "1. When is it okay to tune a hyperparameter on the validation set?\n",
    "1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peeking!)\n",
    "1. What is a regression problem? What loss function should you use for such a problem?\n",
    "1. What do you need to do to make sure the fastai library applies the same data augmentation to your input images and your target point coordinates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Read a tutorial about Pandas DataFrames and experiment with a few methods that look interesting to you. See the book's website for recommended tutorials.\n",
    "1. Retrain the bear classifier using multi-label classification. See if you can make it work effectively with images that don't contain any bears, including showing that information in the web application. Try an image with two different kinds of bears. Check whether the accuracy on the single-label dataset is impacted using multi-label classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "split_at_heading": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
